OpenRaiser/Intern-Atlas huggingface dataset:
Crystal Structure Prediction via Oblivious Local Search
Venue: arXiv cs.CE 2020
Year: 2020

We study Crystal Structure Prediction, one of the major problems in computational chemistry. This is essentially a continuous optimization problem, where many different, simple and sophisticated, methods have been proposed and applied. The simple searching techniques are easy to understand, usually easy to implement, but they can be slow in practice. On the other hand, the more sophisticated approaches perform well in general, however almost all of them have a large number of parameters that require fine tuning and, in the majority of the cases, chemical expertise is needed in order to properly set them up. In addition, due to the chemical expertise involved in the parameter-tuning, these approaches can be {\em biased} towards previously-known crystal structures. Our contribution is twofold. Firstly, we formalize the Crystal Structure Prediction problem, alongside several other intermediate problems, from a theoretical computer science perspective. Secondly, we propose an oblivious algorithm for Crystal Structure Prediction that is based on local search. Oblivious means that our algorithm requires minimal knowledge about the composition we are trying to compute a crystal structure for. In addition, our algorithm can be used as an intermediate step by {\em any} method. Our experiments show that our algorithms outperform the standard basin hopping, a well studied algorithm for the problem.
An enhanced pinwheel algorithm for the bamboo garden trimming problem
Venue: arXiv cs.DS 2020
Year: 2020

In the Bamboo Garden Trimming Problem (BGT), there is a garden populated by n bamboos b(1), b(2), ... , b(n)$ with daily growth rates h(1) >= h(2) >= ... >= h(n). We assume that the initial heights of bamboos are zero. A gardener is in charge of the bamboos and trims them to height zero according to some schedule. The objective is to design a perpetual schedule of trimming so as to maintain the height of the bamboo garden as low as possible. We consider the so-called discrete BGT variant, where the gardener is allowed to trim only one bamboo at the end of each day. For discrete BGT, the current state-of-the-art approximation algorithm exploits the relationship between BGT and the classical Pinwheel scheduling problem and provides a solution that guarantees a 2-approximation ratio. We propose an alternative Pinwheel scheduling algorithm with approximation ratio converging to 12/7 when sum h(j) > > h(1). Also, we show that the approximation ratio of the proposed algorithm never exceeds 32000/16947 approximately 1.888. This is the first algorithm reaching a ratio strictly inferior to 19/10.
AI on the Edge: Rethinking AI-based IoT Applications Using Specialized Edge Architectures
Venue: arXiv cs.DC 2020
Year: 2020

Edge computing has emerged as a popular paradigm for supporting mobile and IoT applications with low latency or high bandwidth needs. The attractiveness of edge computing has been further enhanced due to the recent availability of special-purpose hardware to accelerate specific compute tasks, such as deep learning inference, on edge nodes. In this paper, we experimentally compare the benefits and limitations of using specialized edge systems, built using edge accelerators, to more traditional forms of edge and cloud computing. Our experimental study using edge-based AI workloads shows that today's edge accelerators can provide comparable, and in many cases better, performance, when normalized for power or cost, than traditional edge and cloud servers. They also provide latency and bandwidth benefits for split processing, across and within tiers, when using model compression or model splitting, but require dynamic methods to determine the optimal split across tiers. We find that edge accelerators can support varying degrees of concurrency for multi-tenant inference applications, but lack isolation mechanisms necessary for edge cloud multi-tenant hosting.
Generation of Consistent Sets of Multi-Label Classification Rules with a Multi-Objective Evolutionary Algorithm
Venue: arXiv cs.LG 2020
Year: 2020

Multi-label classification consists in classifying an instance into two or more classes simultaneously. It is a very challenging task present in many real-world applications, such as classification of biology, image, video, audio, and text. Recently, the interest in interpretable classification models has grown, partially as a consequence of regulations such as the General Data Protection Regulation. In this context, we propose a multi-objective evolutionary algorithm that generates multiple rule-based multi-label classification models, allowing users to choose among models that offer different compromises between predictive power and interpretability. An important contribution of this work is that different from most algorithms, which usually generate models based on lists (ordered collections) of rules, our algorithm generates models based on sets (unordered collections) of rules, increasing interpretability. Also, by employing a conflict avoidance algorithm during the rule-creation, every rule within a given model is guaranteed to be consistent with every other rule in the same model. Thus, no conflict resolution strategy is required, evolving simpler models. We conducted experiments on synthetic and real-world datasets and compared our results with state-of-the-art algorithms in terms of predictive performance (F-Score) and interpretability (model size), and demonstrate that our best models had comparable F-Score and smaller model sizes.
Avatar Work: Telework for Disabled People Unable to Go Outside by Using Avatar Robots "OriHime-D" and Its Verification
Venue: arXiv cs.RO 2020
Year: 2020

In this study, we propose a telework "avatar work" that enables people with disabilities to engage in physical works such as customer service in order to realize an inclusive society, where we can do anything if we have free mind, even though we are bedridden. In avatar work, disabled people can remotely engage in physical work by operating a proposed robot "OriHime-D" with a mouse or gaze input depending on their own disabilities. As a social implementation initiative of avatar work, we have opened a two-week limited avatar robot cafe and have evaluated remote employment by people with disabilities using OriHime-D. As the results by 10 people with disabilities, we have confirmed that the proposed avatar work leads to mental fulfillment for people with disparities, and can be designed with adaptable workload. In addition, we have confirmed that the work content of the experimental cafe is appropriate for people with a variety of disabilities seeking social participation. This study contributes to fulfillment all through life and lifetime working, and at the same time leads to a solution to the employment shortage problem.
Adversarial Stress Testing of Lifetime Distributions
Venue: arXiv cs.AI 2020
Year: 2020

In this paper we put forward the viewpoint that the notion of stress testing financial institutions and engineered systems can also be made viable appropos the stress testing an individual's strength of conviction in a probability distribution. The difference is interpretation and perspective. To make our case we consider a game theoretic setup entailing two players, an adversarial C, and an amicable M.The underlying metrics entail a de Finetti style 2 sided bet with asymmetric payoffs as a way to give meaning to lifetime distributions, an adversarial stress testing function, and a maximization of the expected utility of betting scores via the Kullback Liebler discrimination.
Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions
Venue: arXiv cs.CV 2020
Year: 2020

Convolutional neural networks (CNNs) have a large number of variables and hence suffer from a complexity problem for their implementation. Different methods and techniques have developed to alleviate the problem of CNN's complexity, such as quantization, pruning, etc. Among the different simplification methods, computation in the Fourier domain is regarded as a new paradigm for the acceleration of CNNs. Recent studies on Fast Fourier Transform (FFT) based CNN aiming at simplifying the computations required for FFT. However, there is a lot of space for working on the reduction of the computational complexity of FFT. In this paper, a new method for CNN processing in the FFT domain is proposed, which is based on input splitting. There are problems in the computation of FFT using small kernels in situations such as CNN. Splitting can be considered as an effective solution for such issues aroused by small kernels. Using splitting redundancy, such as overlap-and-add, is reduced and, efficiency is increased. Hardware implementation of the proposed FFT method, as well as different analyses of the complexity, are performed to demonstrate the proper performance of the proposed method.
Combining Visible and Infrared Spectrum Imagery using Machine Learning for Small Unmanned Aerial System Detection
Venue: arXiv cs.CV 2020
Year: 2020

Advances in machine learning and deep neural networks for object detection, coupled with lower cost and power requirements of cameras, led to promising vision-based solutions for sUAS detection. However, solely relying on the visible spectrum has previously led to reliability issues in low contrast scenarios such as sUAS flying below the treeline and against bright sources of light. Alternatively, due to the relatively high heat signatures emitted from sUAS during flight, a long-wave infrared (LWIR) sensor is able to produce images that clearly contrast the sUAS from its background. However, compared to widely available visible spectrum sensors, LWIR sensors have lower resolution and may produce more false positives when exposed to birds or other heat sources. This research work proposes combining the advantages of the LWIR and visible spectrum sensors using machine learning for vision-based detection of sUAS. Utilizing the heightened background contrast from the LWIR sensor combined and synchronized with the relatively increased resolution of the visible spectrum sensor, a deep learning model was trained to detect the sUAS through previously difficult environments. More specifically, the approach demonstrated effective detection of multiple sUAS flying above and below the treeline, in the presence of heat sources, and glare from the sun. Our approach achieved a detection rate of 71.2 +- 8.3%, improving by 69% when compared to LWIR and by 30.4% when visible spectrum alone, and achieved false alarm rate of 2.7 +- 2.6%, decreasing by 74.1% and by 47.1% when compared to LWIR and visible spectrum alone, respectively, on average, for single and multiple drone scenarios, controlled for the same confidence metric of the machine learning object detector of at least 50%. Videos of the solution's performance can be seen at https://sites.google.com/view/tamudrone-spie2020/.
Towards an immersive user interface for waypoint navigation of a mobile robot
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we investigate the utility of head-mounted display (HMD) interfaces for navigation of mobile robots. We focus on the selection of waypoint positions for the robot, whilst maintaining an egocentric view of the robot's environment. Inspired by virtual reality (VR) gaming, we propose a target selection method that uses the 6 degrees-of-freedom tracked controllers of a commercial VR headset. This allows an operator to point to the desired target position, in the vicinity of the robot, which the robot then autonomously navigates towards. A user study (37 participants) was conducted to examine the efficacy of this control strategy when compared to direct control, both with and without a communication delay. The results of the experiment showed that participants were able to learn how to use the novel system quickly, and the majority of participants reported a preference for waypoint control. Across all recorded metrics (task performance, operator workload and usability) the proposed waypoint control interface was not significantly affected by the communication delay, in contrast to direct control. The simulated experiment indicated that a real-world implementation of the proposed interface could be effective, but also highlighted the need to manage the negative effects of HMDs - particularly VR sickness.
Vectorization and Minimization of Memory Footprint for Linear High-Order Discontinuous Galerkin Schemes
Venue: arXiv cs.MS 2020
Year: 2020

We present a sequence of optimizations to the performance-critical compute kernels of the high-order discontinuous Galerkin solver of the hyperbolic PDE engine ExaHyPE -- successively tackling bottlenecks due to SIMD operations, cache hierarchies and restrictions in the software design. Starting from a generic scalar implementation of the numerical scheme, our first optimized variant applies state-of-the-art optimization techniques by vectorizing loops, improving the data layout and using Loop-over-GEMM to perform tensor contractions via highly optimized matrix multiplication functions provided by the LIBXSMM library. We show that memory stalls due to a memory footprint exceeding our L2 cache size hindered the vectorization gains. We therefore introduce a new kernel that applies a sum factorization approach to reduce the kernel's memory footprint and improve its cache locality. With the L2 cache bottleneck removed, we were able to exploit additional vectorization opportunities, by introducing a hybrid Array-of-Structure-of-Array data layout that solves the data layout conflict between matrix multiplications kernels and the point-wise functions to implement PDE-specific terms. With this last kernel, evaluated in a benchmark simulation at high polynomial order, only 2\% of the floating point operations are still performed using scalar instructions and 22.5\% of the available performance is achieved.
Towards Automating the AI Operations Lifecycle
Venue: arXiv cs.LG 2020
Year: 2020

Today's AI deployments often require significant human involvement and skill in the operational stages of the model lifecycle, including pre-release testing, monitoring, problem diagnosis and model improvements. We present a set of enabling technologies that can be used to increase the level of automation in AI operations, thus lowering the human effort required. Since a common source of human involvement is the need to assess the performance of deployed models, we focus on technologies for performance prediction and KPI analysis and show how they can be used to improve automation in the key stages of a typical AI operations pipeline.
A Close Look at Deep Learning with Small Data
Venue: arXiv cs.LG 2020
Year: 2020

In this work, we perform a wide variety of experiments with different deep learning architectures on datasets of limited size. According to our study, we show that model complexity is a critical factor when only a few samples per class are available. Differently from the literature, we show that in some configurations, the state of the art can be improved using low complexity models. For instance, in problems with scarce training samples and without data augmentation, low-complexity convolutional neural networks perform comparably well or better than state-of-the-art architectures. Moreover, we show that even standard data augmentation can boost recognition performance by large margins. This result suggests the development of more complex data generation/augmentation pipelines for cases when data is limited. Finally, we show that dropout, a widely used regularization technique, maintains its role as a good regularizer even when data is scarce. Our findings are empirically validated on the sub-sampled versions of popular CIFAR-10, Fashion-MNIST and, SVHN benchmarks.
Making RooFit Ready for Run 3
Venue: arXiv cs.MS 2020
Year: 2020

RooFit and RooStats, the toolkits for statistical modelling in ROOT, are used in most searches and measurements at the Large Hadron Collider. The data to be collected in Run 3 will enable measurements with higher precision and models with larger complexity, but also require faster data processing. In this work, first results on modernising RooFit's collections, restructuring data flow and vectorising likelihood fits in RooFit will be discussed. These improvements will enable the LHC experiments to process larger datasets without having to compromise with respect to model complexity, as fitting times would increase significantly with the large datasets to be expected in Run 3.
Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection
Venue: arXiv cs.CV 2020
Year: 2020

Recent advances in the area of plane segmentation from single RGB images show strong accuracy improvements and now allow a reliable segmentation of indoor scenes into planes. Nonetheless, fine-grained details of these segmentation masks are still lacking accuracy, thus restricting the usability of such techniques on a larger scale in numerous applications, such as inpainting for Augmented Reality use cases. We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects. Our approach is motivated by logistics, where this assumption is valid and refined planes can be used to perform robust object detection without the need for supervised learning. Results for two baselines and our approach are reported on our own dataset, which we made publicly available. The results show a consistent improvement over the state-of-the-art. The influence of the prior segmentation and the edge detection is investigated and finally, areas for future research are proposed.
Algorithmic Counting of Zero-Dimensional Finite Topological Spaces With Respect to the Covering Dimension
Venue: arXiv cs.DM 2020
Year: 2020

Taking the covering dimension dim as notion for the dimension of a topological space, we first specify thenumber zdim_{T_0}(n) of zero-dimensional T_0-spaces on {1,...,n}$ and the number zdim(n) of zero-dimensional arbitrary topological spaces on {1,\ldots,n} by means oftwo mappings po and P that yieldthe number po(n) of partial orders on {1,...,n} and the set P(n) of partitions of {1,...,n}, respectively. Algorithms for both mappings exist. Assuming one for po to be at hand, we use our specification of zdim_{T_0}(n) and modify one for P in such a way that it computes zdim_{T_0}(n) instead of P(n). The specification of zdim(n) then allows to compute this number from zdim_{T_0}(1) to zdim_{T_0}(n) and the Stirling numbers of the second kind S(n,1) to S(n,n). The resulting algorithms have been implemented in C and we also present results of practical experiments with them. To considerably reduce the running times for computing zdim_{T_0}(n), we also describe a backtracking approach and its parallel implementation in C using the OpenMP library.
EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning
Venue: arXiv cs.NI 2020
Year: 2020

5G and edge computing will serve various emerging use cases that have diverse requirements of multiple resources, e.g., radio, transportation, and computing. Network slicing is a promising technology for creating virtual networks that can be customized according to the requirements of different use cases. Provisioning network slices requires end-to-end resource orchestration which is challenging. In this paper, we design a decentralized resource orchestration system named EdgeSlice for dynamic end-to-end network slicing. EdgeSlice introduces a new decentralized deep reinforcement learning (D-DRL) method to efficiently orchestrate end-to-end resources. D-DRL is composed of a performance coordinator and multiple orchestration agents. The performance coordinator manages the resource orchestration policies in all the orchestration agents to ensure the service level agreement (SLA) of network slices. The orchestration agent learns the resource demands of network slices and orchestrates the resource allocation accordingly to optimize the performance of the slices under the constrained networking and computing resources. We design radio, transport and computing manager to enable dynamic configuration of end-to-end resources at runtime. We implement EdgeSlice on a prototype of the end-to-end wireless edge computing network with OpenAirInterface LTE network, OpenDayLight SDN switches, and CUDA GPU platform. The performance of EdgeSlice is evaluated through both experiments and trace-driven simulations. The evaluation results show that EdgeSlice achieves much improvement as compared to baseline in terms of performance, scalability, compatibility.
IoT Blockchain Solution for Air Quality Monitoring in SmartCities
Venue: arXiv cs.NI 2020
Year: 2020

IoT cloud enabled societal applications have dramatically increased in the recent past due to the thrust for innovations, notably through startup initiatives, in various sectors such as agriculture, healthcare, industry, and so forth. The existing IoT cloud solutions have led practitioners or researchers to a haphazard clutter of serious security hazards and performance inefficiencies. This paper proposes a blockchain enabled IoT cloud implementation to tackle the existing issues in smart cities. It particularly highlights the implementation of chaincodes for air quality monitoring systems in SmartCities; the proposed architecture named as IoT enabled Blockchain for Air Quality Monitoring System (IB-AQMS) is illustrated using experiments. Experimental results were carried out and the findings were disclosed in the paper.
SuperNet -- An efficient method of neural networks ensembling
Venue: arXiv cs.LG 2020
Year: 2020

The main flaw of neural network ensembling is that it is exceptionally demanding computationally, especially, if the individual sub-models are large neural networks, which must be trained separately. Having in mind that modern DNNs can be very accurate, they are already the huge ensembles of simple classifiers, and that one can construct more thrifty compressed neural net of a similar performance for any ensemble, the idea of designing the expensive SuperNets can be questionable. The widespread belief that ensembling increases the prediction time, makes it not attractive and can be the reason that the main stream of ML research is directed towards developing better loss functions and learning strategies for more advanced and efficient neural networks. On the other hand, all these factors make the architectures more complex what may lead to overfitting and high computational complexity, that is, to the same flaws for which the highly parametrized SuperNets ensembles are blamed. The goal of the master thesis is to speed up the execution time required for ensemble generation. Instead of training K inaccurate sub-models, each of them can represent various phases of training (representing various local minima of the loss function) of a single DNN [Huang et al., 2017; Gripov et al., 2018]. Thus, the computational performance of the SuperNet can be comparable to the maximum CPU time spent on training its single sub-model, plus usually much shorter CPU time required for training the SuperNet coupling factors.